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One of the essential duties of the State of California Department of Civil Defense in times of nuclear weapon attack will be radiological monitoring. The workers in this field will operate from a mobile laboratory truck, equipped with radiation counters and meters, but containing only the simplest of chemical equipment. One of their main problems will be the determination of the total amount of radioactive material, especially fallout of fission products, in a water sample. As yet no method of analysis both simple and rapid enough for application to mobile operation has been developed. It was the purpose of this research to develop and refine a simple, rapid, quantitative procedure for the detection and estimation of the total amount of radioactive material contained in a water sample, using only readily available and preferably inexpensive apparatus. However, no attempt has been made to either qualitatively or quantitatively determine any of the possible individual constituents.

No description available.

In this study, two methods of analysing activity concentrations of natural radionuclides (U, Th and K) in soil are critically compared. These are the Window Analysis (WA) and Full Spectrum Analysis (FSA). In the usual WA method, the activity concentrations are determined from the net counts of the windows set around individual &gamma / -ray peaks associated with the decay of U, Th and K. In the FSA method, the full energy spectrum is considered and the measured spectrum is described as the sum of the three standard spectra (associated with U, Th and K, respectively), each multiplied by an unknown concentration. The concentrations are determined from the FSA and correspond to the activity concentrations of U, Th and K in the soil. The standard spectra derived from separate calibration measurements using the HPGe detector, represents the response of the HPGe to a Marinelli sample beaker containing an activity concentration of 1 Bq/kg.

Thesis (M. Phil.)--University of Hong Kong, 2007. / Title proper from title frame. Also available in printed format.

In this study, two methods of analysing activity concentrations of natural radionuclides (U, Th and K) in soil are critically compared. These are the Window Analysis (WA) and Full Spectrum Analysis (FSA). In the usual WA method, the activity concentrations are determined from the net counts of the windows set around individual &gamma / -ray peaks associated with the decay of U, Th and K. In the FSA method, the full energy spectrum is considered and the measured spectrum is described as the sum of the three standard spectra (associated with U, Th and K, respectively), each multiplied by an unknown concentration. The concentrations are determined from the FSA and correspond to the activity concentrations of U, Th and K in the soil. The standard spectra derived from separate calibration measurements using the HPGe detector, represents the response of the HPGe to a Marinelli sample beaker containing an activity concentration of 1 Bq/kg.

This thesis describes the development and application of novel techniques to reduce the EEG artefacts at source during the simultaneous acquisition of EEG and fMRI data. The work described in this thesis was carried out by the author in the Sir Peter Mansfield Magnetic Resonance Centre, School of Physics & Astronomy at the University of Nottingham, between October 2010 and January 2013. Large artefacts compromise EEG data quality during simultaneous fMRI. These artefact voltages pose heavy demands on the bandwidth and dynamic range of EEG amplifiers and mean that even small fractional variations in the artefact voltages give rise to significant residual artefacts after correction, which can easily swamp signals from brain activity. Therefore any intrinsic reduction in the magnitude of the artefacts would be highly advantageous, allowing data with a higher bandwidth to be acquired without amplifier saturation, and facilitating improved detection of brain activity. This thesis firstly explores a new method for reducing the gradient artefact (GA), which is induced in EEG data recorded during concurrent MRI, by investigating the effects of the cable configuration on the characteristics of the GA. This work showed that the GA amplitude and its sensitivity to movement of the cabling is reduced by minimising wire loop areas in the cabling between the EEG cap and amplifier. Another novel approach for reducing the magnitude and variability of the artefacts is the use of an EEG cap that incorporates electrodes embedded in a reference layer, which has a similar conductivity to tissue and is electrically isolated from the scalp. With this arrangement, the artefact voltages produced on the reference layer leads are theoretically similar to those induced in the scalp leads, but neuronal signals are not detected in the reference layer. Therefore taking the difference of the voltages in the reference and scalp channels should reduce the artefacts, without affecting sensitivity to neuronal signals. The theoretical efficacy of artefact correction that can be achieved by using this new reference layer artefact subtraction (RLAS) method was investigated. This was done through separate electromagnetic simulations of the artefacts induced in a hemispherical reference layer and a spherical volume conductor in a time-varying magnetic field and the results showed that similar artefacts are induced on the surface of both conductors. Simulations are also performed to find the optimal design for an RLAS system, by varying the geometry of the system. A simple experimental realisation of the RLAS system was implemented to investigate the degree of artefact attenuation that can be achieved via RLAS. Through a series of experiments on phantoms and human subjects, it is shown here that RLAS significantly reduces the GA, pulse (PA) and motion (MA) artefacts, while allowing accurate recording of neuronal signals. The results indicate that RLAS generally outperforms the standard artefact correction method, average artefact subtraction (AAS), in the removal of the GA and PA when motion is present, while the combination of RLAS and AAS always produces higher artefact attenuation than AAS alone. Additionally, this work demonstrates that RLAS greatly attenuates the unpredictable and highly variable MA that are very hard to remove using post-processing methods.